Separating Clear Customer Signals from the Noise of Big Data

Customer data comes flooding into businesses at a greater volume and velocity than ever before, as an array of new channels for outreach and interaction open up a wealth of opportunities for businesses to learn more about the clients they serve.

Customer data comes flooding into businesses at a greater volume and velocity than ever before, as an array of new channels for outreach and interaction open up a wealth of opportunities for businesses to learn more about the clients they serve.

Ideally, this abundance of data delivers valuable insights that decision makers can take action on to improve customer service and experience, not to mention internal operations that can significantly streamline workflows. In many cases, however, there is an overabundance of data, which makes gleaning meaningful insights all the more difficult when effective vetting processes aren’t in place.

Experts estimate that “bad” or inaccurate data costs businesses in the United States upwards of $700 billion annually, with an average impact on individual companies coming in at roughly $13.3 million. While these are the hard figures that indicate losses most directly associated with the collection of inaccurate data, there are other intangible factors that aren’t taken into account. Damage to reputation among vital customers, for instance, as a result of misinformation can have significant long-term repercussions on a business’ bottom line that could take years to resolve.

The implications of data quality are paramount for organizations today, as big data plays a larger role than ever in business planning and strategy. This puts a lot of responsibility on the data analysts and data scientists tasked with vetting customer information as it comes in, as they’ll need to adapt to new ways of thinking in order to ensure data quality at such a massive scale.

These individuals will need to learn how to blend traditional questions regarding data quality with a new way of thinking, adding veracity to their big data by connecting this information with trusted data quality tools. This will help businesses form a single customer view of each client they serve, which is invaluable to assuring customer needs are met and the business relationship continues to benefit both parties.

To discuss the tools and methods for ensuring data quality in an era where data quantity is so massive for many organizations, we hosted an Information Management webinar entitled “Clear Customer Signals from Big Data Noise – How It’s Done,” on May 23rd 2017. During the webinar, we looked at the practical steps analysts can take today to improve decision making across their organization.

Be sure to listen to the webinar, hosted by Pitney Bowes Information Management, our experts for your pressing data quality questions. Top practitioners of customer-focused analytics were on hand, so don’t miss out on this opportunity to learn from those on the forefront of the big data deluge.

Most likely, your business faces tough marketing questions: Where are my most profitable customers? What do they need? How do I reach more of them? Answers to these and many other questions lie in a richer view of customers and prospects.